Method and apparatus for selecting links to include in a probabilistic generative model for text
First Claim
1. A method for selecting links while updating a probabilistic generative model for textual documents, comprising:
- receiving a current model, which contains terminal nodes representing words and cluster nodes representing clusters of conceptually related words, wherein nodes in the current model are coupled together by weighted links, wherein if a node fires, a link from the node to another node is activated and causes the other node to fire with a probability proportionate to the weight of the link;
applying a set of training documents containing words to the current model to produce a new model, and while doing so,determining expected counts for activations of links and prospective links,determining link-ratings for the links and the prospective links based on the expected counts, andselecting links to be included in the new model based on the determined link-ratings; and
making the new model the current model.
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Abstract
Some embodiments of the present invention provide a system that selects links while updating a probabilistic generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing words and cluster nodes representing clusters of conceptually related words, wherein nodes in the current model are coupled together by weighted links, wherein if a node fires, a link from the node to another node is activated and causes the other node to fire with a probability proportionate to the weight of the link. Next, the system applies a set of training documents containing words to the current model to produce a new model. While doing so, the system: determines expected counts for activations of links and prospective links; determines link-ratings for the links and the prospective links based on the expected counts, and selects links to be included in the new model based on the determined link-ratings. Finally, the system makes the new model the current model.
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Citations
33 Claims
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1. A method for selecting links while updating a probabilistic generative model for textual documents, comprising:
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receiving a current model, which contains terminal nodes representing words and cluster nodes representing clusters of conceptually related words, wherein nodes in the current model are coupled together by weighted links, wherein if a node fires, a link from the node to another node is activated and causes the other node to fire with a probability proportionate to the weight of the link; applying a set of training documents containing words to the current model to produce a new model, and while doing so, determining expected counts for activations of links and prospective links, determining link-ratings for the links and the prospective links based on the expected counts, and selecting links to be included in the new model based on the determined link-ratings; and making the new model the current model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for selecting links while updating a probabilistic generative model for textual documents, the method comprising:
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receiving a current model, which contains terminal nodes representing words and cluster nodes representing clusters of conceptually related words, wherein nodes in the current model are coupled together by weighted links, wherein if a node fires, a link from the node to another node is activated and causes the other node to fire with a probability proportionate to the weight of the link; applying a set of training documents containing words to the current model to produce a new model, and while doing so, determining expected counts for activations of links and prospective links, determining link-ratings for the links and the prospective links based on the expected counts, and selecting links to be included in the new model based on the determined link-ratings; and making the new model the current model. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. An apparatus that selects links while updating a probabilistic generative model for textual documents, comprising:
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a data store configured to store a current model, which contains terminal nodes representing words and cluster nodes representing clusters of conceptually related words, wherein nodes in the current model are coupled together by weighted links, wherein if a node fires, a link from the node to another node is activated and causes the other node to fire with a probability proportionate to the weight of the link; a training mechanism configured to apply a set of training documents containing words to the current model to produce a new model, wherein the training mechanism is configured to, determine expected counts for activations of links and prospective links, determine link-ratings for the links and the prospective links based on the expected counts, and select links to be included in the new model based on the determined link-ratings; and an updating mechanism configured to make the new model the current model. - View Dependent Claims (24, 25, 26, 27, 28, 29, 30, 31, 32, 33)
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Specification